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Monitoring irrigation is crucial for sustainable water management in freshwater-limited regions. Even though soil moisture (SM)-based inversion algorithms have been widely used to estimate irrigation, scarcity of irrigation records has prevented a thorough understanding of their uncertainties, especially in humid regions. This study assesses the suitability of the SM2RAIN algorithm for estimating irrigation at field scale using high-temporal-resolution data from four corn growing experiments conducted in north-central Florida. Daily irrigation estimates were compared with observations, revealing root mean squared differences of 1.26 to 3.84 mm/day and Nash–Sutcliffe Efficiencies of 0.33 to 0.89. The estimates were more sensitive to uncertainties in static inputs of porosity, saturation moisture and soil thickness than they were to noise in time series inputs. Defining the saturation moisture as porosity made the algorithm insensitive to both parameters, while increasing soil thickness from 40 to 200 mm improved detection accuracies by 34–46%. In addition, the impact of SM on the estimations was investigated based on satellite overpass times. The analysis showed that morning passes produced more accurate estimates for the study site, while evening passes doubled the uncertainty. This study enhances the understanding of the SM2RAIN algorithm for irrigation estimation in subtropical humid conditions, guiding future high-resolution applications. [ABSTRACT FROM AUTHOR]

Groundwater modeling is often associated with uncertainties due to incomplete knowledge of the subsurface system or uncertainties arising from variability in model system processes and field conditions. So far, not much research has been conducted to investigate the uncertainty of the groundwater flow model. Studies in this field focus on statistical methods. Due to the need to investigate the uncertainty to obtain reliable results, this study presents an approach to evaluate the uncertainty parameters of the groundwater flow model. In this way, modified GLUE (MGLUE) method as the uncertainty assessment method is linked to the meshless local Petrov–Galerkin (MLPG) as the simulation model. The method (which is called MGLUE-MLPG) is applied to two aquifers. In the first aquifer, three parameters of the meshless flow model (parameters related to the numerical method e.g., sizes of the integration and weight subdomains and the amount of the penalty coefficient) are identified as uncertainty parameters. The results indicate that these three parameters have a high degree of uncertainty, so their coefficients of variation are 72.37, 19.92 and 51.55, respectively. In the second standard aquifer, in addition to the numerical parameters, the transmissivity coefficients (model parameters) in two different directions (horizontal and vertical directions) are taken into account in the uncertainty model. The results show more uncertainty for the numerical parameters and do not show many changes in the transmissivity coefficient parameter. This means that the box diagrams of these parameters are almost the same. After precise values have been reached, groundwater flow was simulated. The obtained results are so accurate as to indicate the importance of using this model for all aquifers. In the first aquifer, the root mean square error (RMSE) values for MGLUE-MLPG and finite element method (FEM) are 0.236 and 0.249 m, respectively. The second aquifer shows higher accuracy than the other numerical method, so the RMSE values for MGLUE-MLPG and PPCM are 0.0060 and 0.0121 m, respectively. [ABSTRACT FROM AUTHOR]

Groundwater resources in Mediterranean coastal aquifers are under several threats including saltwater intrusion. This situation is exacerbated by the absence of sustainable management plans for groundwater resources. Management and monitoring of groundwater systems require an integrated approach and the joint interpretation of any available information. This work investigates how uncertainty can be integrated within the geo-modelling workflow when creating numerical three-dimensional aquifer models with electrical resistivity borehole logs, geostatistical simulation and Bayesian model averaging. Multiple geological scenarios of electrical resistivity are created with geostatistical simulation by removing one borehole at a time from the set of available boreholes. To account for the spatial uncertainty simultaneously reflected by the multiple geostatistical scenarios, Bayesian model averaging is used to combine the probability distribution functions of each scenario into a global one, thus providing more credible uncertainty intervals. The proposed methodology is applied to a water-stressed groundwater system located in Crete that is threatened by saltwater intrusion. The results obtained agree with the general knowledge of this complex environment and enable sustainable groundwater management policies to be devised considering optimistic and pessimistic scenarios. [ABSTRACT FROM AUTHOR]

Surface electromyography is an important and widely used biomedical measurement technique. In biomedical engineering, obtaining high-quality EMG data and processing it in real-time is challenging. Their calibration repeatability and reliability, on the other hand, remain in discussion. Furthermore, the metrological elements of EMG systems have not yet been studied. In this research work, a portable EMG acquisition module has been developed, and the uncertainty of two EMG characteristics for four everyday hand motions has been investigated. Furthermore, an inter-instrument comparison has been carried out in a controlled environment. For multiple trials, the result has been estimated for 10 people. While designing the experiment, two major uncertainty factors, muscle selection/electrode placement and subjective analysis, have been taken into consideration. The developed system's inter-instrument performance has been found to be similar to that of the existing system (p-value > 0.05). The total uncertainty of the developed system has been found to be in the range of 0.0106–0.0196% (p-value > 0.05) for RMS and 0.1001–0.2084% (p-value > 0.05) for MF. The existing commercial system, in contrast, exhibited uncertainty in the range of 0.0171–0.0359% (p-value > 0.05) for RMS and 0.1010–0.2088% (p-value > 0.05) for MF. The developed EMG acquisition system's performance and cost-effectiveness validate its utility and acceptability for low-cost product development. [ABSTRACT FROM AUTHOR]

Maize (Zea mays) is an important staple crop for food security in Sub-Saharan Africa. However, there is need to increase production to feed a growing population. In Ghana, this is mainly done by increasing acreage with adverse environmental consequences, rather than yield increment per unit area. Accurate prediction of maize yields and nutrient use efficiency in production is critical to making informed decisions toward economic and ecological sustainability. We trained the random forest machine learning algorithm to predict maize yield and agronomic efficiency in Ghana using soil, climate, environment, and management factors, including fertilizer application. We calibrated and evaluated the performance of the random forest machine learning algorithm using a 5 × 10-fold nested cross-validation approach. Data from 482 maize field trials consisting of 3136 georeferenced treatment plots conducted in Ghana from 1991 to 2020 were used to train the algorithm, identify important predictor variables, and quantify the uncertainties associated with the random forest predictions. The mean error, root mean squared error, model efficiency coefficient and 90 % prediction interval coverage probability were calculated. The results obtained on test data demonstrate good prediction performance for yield (MEC = 0.81) and moderate performance for agronomic efficiency (MEC = 0.63, 0.55 and 0.54 for AE-N, AE-P and AE-K, respectively). We found that climatic variables were less important predictors than soil variables for yield prediction, but temperature was of key importance to yield prediction and rainfall to agronomic efficiency. The developed random forest models provided a better understanding of the drivers of maize yield and agronomic efficiency in a tropical climate and an insight towards improving fertilizer recommendations for sustainable maize production and food security in Sub-Saharan Africa.

To properly evaluate the quantity of mineral resources in a deposit, modeling the rock units of the deposit is essential since it informs the deposit's mineralization controls. The deterministic methods of modeling include geological assumptions, especially between drill holes, and therefore, might predict the deposit's complexity biasedly. Additionally, the uncertainty of rock units at unsampled locations cannot be quantified. Truncated/plurigaussian Gaussian and sequential indicator simulations are two most frequent stochastic approaches better suited to address these shortcomings. The ability to precisely depict the spatial correlation between rock units and quantify their uncertainties has made the truncated Gaussian simulation an essential technique for delineating rock units. However, using this method, long-range geological features like veins, faults, and fractures are often loosely modeled thus necessitating the use of soft data such as deterministic interpretive geological model to avert this inaccuracy. In this paper, the truncated Gaussian simulation is used to assess the spatial variability of rock units and quantify the uncertainty in their occurrence in a vein-dominated gold deposit. A novel approach is presented to model the rock units, particularly the veins, by incorporating a collocated co-simulation algorithm via a global correlation coefficient parameter. The realizations are conditioned to drill hole data and a collocated interpretive geological model created by a machine learning algorithm. The outcome shows realistic reproduction of the veins and other rock units, as representing the drill hole data and assessing their uncertainties thereof. [ABSTRACT FROM AUTHOR]

The exposure assessment component of a Wildlife Ecological Risk Assessment aims to estimate the magnitude, frequency, and duration of exposure to a chemical or environmental contaminant, along with characteristics of the exposed population. This can be challenging in wildlife as there is often high uncertainty and error caused by broad‐based, interspecific extrapolation and assumptions often because of a lack of data. Both the US Environmental Protection Agency (USEPA) and European Food Safety Authority (EFSA) have broadly directed exposure assessments to include estimates of the quantity (dose or concentration), frequency, and duration of exposure to a contaminant of interest while considering "all relevant factors." This ambiguity in the inclusion or exclusion of specific factors (e.g., individual and species‐specific biology, diet, or proportion time in treated or contaminated area) can significantly influence the overall risk characterization. In this review, we identify four discrete categories of complexity that should be considered in an exposure assessment—chemical, environmental, organismal, and ecological. These may require more data, but a degree of inclusion at all stages of the risk assessment is critical to moving beyond screening‐level methods that have a high degree of uncertainty and suffer from conservatism and a lack of realism. We demonstrate that there are many existing and emerging scientific tools and cross‐cutting solutions for tackling exposure complexity. To foster greater application of these methods in wildlife exposure assessments, we present a new framework for risk assessors to construct an "exposure matrix." Using three case studies, we illustrate how the matrix can better inform, integrate, and more transparently communicate the important elements of complexity and realism in exposure assessments for wildlife. Modernizing wildlife exposure assessments is long overdue and will require improved collaboration, data sharing, application of standardized exposure scenarios, better communication of assumptions and uncertainty, and postregulatory tracking. Integr Environ Assess Manag 2024;20:674–698. © 2023 SETAC Key Points: Exposure assessments for wildlife commonly rely on conservative and overly simplistic model species‐ and site‐specific assumptions that lack scientific rigor resulting in great uncertainty about true exposure conditions.We identify four major categories of exposure complexity that should comprise an exposure assessment—chemical, environmental, organismal, and ecological—and provide tools that can better address these themes.To improve wildlife conservation and environmental quality, exposure assessments must better use available open access data; move from a tier‐based to a standardized, scenario‐based matrix assessment framework; and routinely quantify uncertainty to improve transparency and decision‐making. [ABSTRACT FROM AUTHOR]

In anaerobic technology, pH values are crucial for targeted volatile fatty acid production. While pH dynamics can be modeled using the Anaerobic Digestion Model No. 1 (ADM1), simulation results may be biased. To address this issue, the pH prediction routine of Visual Water, a specialized water chemistry simulator, was validated. Unlike ADM1, it accounts for ionic strength and activities while also providing an automated uncertainty analysis. The analysis revealed Visual Water simulations to better fit measured pH data from acidic solutions in a miniaturized stirred‐tank reactor. [ABSTRACT FROM AUTHOR]

This article presents a method for evaluating the environmental benefits of implementing Product-Service Systems (PSSs) and Smart PSS with application to the heating systems field. The proposed PSS-oriented life cycle assessment method aims at addressing the specificities of PSS and Smart PSS, representing PSS variety and dealing with uncertainty sources resulting from the PSS context. Besides environmental analysis, the method supports decision-making by comparing different PSS scenarios. This study focuses on the challenges of a generic configuration of the life cycle assessment method and the rigorous handling of uncertainty sources, while an industrial case study reports a real case of PSS design decision-making.Article Highlights: Adapting LCA in the context of smart PSS Product-Service Systems appear as a Strong Environmental Driver Advancing Environmental Sustainability through Comprehensive PSS Design [ABSTRACT FROM AUTHOR]

Purpose: Potentially contradictory indicators in Life Cycle Assessment cause ambiguity and thus uncertainty regarding the interpretation of results. The weighting-based ecological scarcity method (ESM) aims at reducing interpretation uncertainty by applying policy-based normative target values. However, the definition of these target values is uncertain due to different reasons such as questionable temporal representativeness. By means of an uncertainty analysis, this paper examines if ESMs are an appropriate approach to support robust decisions on multidimensional environmental impacts. Methods: To assess the effect of uncertain target values (inputs) on environmental indicators (output), the ESM based Life Cycle Impact Assessment (LCIA) is combined with a Monte Carlo Analysis. The comprehensive uncertainty analysis includes the following steps: (1) sample generation, (2) output calculation and (3) results analysis and visualisation. (1) To generate a sample, moderate and strict limits for target values are derived from laws, directives or strategies. Random input parameters are drawn from a uniform distribution within those limits. (2) The sample is used to conduct several LCIAs leading to a distribution of total impact scores. (3) The results' robustness is evaluated by means of the rank acceptability index to identify stable ranks for energy generation systems taken from ecoinvent v. 3.7.1. Results and discussion: Applying moderate and strict target values in the ESM, results in substantial differences in the weighting sets. Even though the application of stricter target values changes the contribution of an environmental indicator to the total impact score the ranking of the energy generation systems varies only slightly. Moreover, the Monte Carlo Analysis reveals that displacement effects in ranks are not arbitrary: systems switch at most between ranks next to each other and most of the analysed systems dominate at least a single rank. Technologies with high shares of land use, global warming and air pollutants and particulate matter show a higher rank variance. Conclusions: The weighting schemes, deduced from target values, provide a meaningful ranking of alternatives. At the same time, the results are not excessively sensitive to the uncertainties of the target values, i.e. the inherent uncertainty of the target values does not result in arbitrary outcomes, which is necessary to support robust decisions. The ESM is able to effectively facilitate decision making by making different environmental issues comparable. [ABSTRACT FROM AUTHOR]

Mapping tree species diversity is essential for monitoring and managing forest ecosystems. Automating ecoforestry mapping using remote sensing images remains an important challenge due to the tremendous variability in forest covers and the conditions under which images used for classification are acquired. Deep learning algorithms have been increasingly used over the last few years to analyse remote sensing data for mapping tree species. However, most of these studies focus only on a small number of species or a limited area and avoid providing a spatially explicit representation of the uncertainty related to the predictions, rendering them unsuitable for operational use.In this study, we used an ensemble of convolutional neural networks to map forest species and land cover types across a 10,000 km2 area in Quebec, Canada, spanning mixed and boreal forests. We built a georeferenced label database to train and test nine models, which resulted from the combination of three training datasets and three-commonly used convolutional neural network architectures (VGG16, ResNet50v2, Densenet121). These models were trained on multiband aerial photographs and a canopy height model derived from airborne lidar point clouds and used to map the diversity and distribution of tree species and land cover types. The level of agreement among models was used to generate uncertainty maps. The performance of the super-ensemble using 1311 independent forest inventory plots and assessed the extent to which uncertainty maps could serve as a spatially explicit indicator of model performance.The super-ensemble achieved 90% global accuracy. Our results indicated that the performance of the super-ensemble surpassed that of all individual architectures while also showing a positive effect of the canopy height model on performance. The comparison of the super-ensemble map with the proportion of basal area measured in forest inventory plots confirmed the reliability of the model over the study area. Our results also indicated that mapping the inter-model agreement provides a reliable spatially explicit estimate of model performance. The robustness and reliability of the proposed approach support its use in an operational context while also providing a conceptual framework to evaluate the reliability of uncertainty maps.

Environmental perception plays a fundamental role in decision-making and is crucial for ensuring the safety of autonomous driving. A pressing challenge is the online evaluation of perception uncertainty, a crucial step towards ensuring the safety and the industrialization of autonomous driving. High-definition maps offer precise information about static elements on the road, along with their topological relationships. As a result, the map can provide valuable prior information for assessing the uncertainty associated with static elements. In this paper, a method for evaluating perception uncertainty online, encompassing both static and dynamic elements, is introduced based on the high-definition map. The proposed method is as follows: Firstly, the uncertainty of static elements in perception, including the uncertainty of their existence and spatial information, was assessed based on the spatial and topological features of the static environmental elements; secondly, an online assessment model for the uncertainty of dynamic elements in perception was constructed. The online evaluation of the static element uncertainty was utilized to infer the dynamic element uncertainty, and then a model for recognizing the driving scenario and weather conditions was constructed to identify the triggering factors of uncertainty in real-time perception during autonomous driving operations, which can further optimize the online assessment model for perception uncertainty. The verification results on the nuScenes dataset show that our uncertainty assessment method based on a high-definition map effectively evaluates the real-time perception results' performance. [ABSTRACT FROM AUTHOR]

Accurate pH calculations are essential for scientists across different disciplines to design optimal reactor solutions, but they can be arduous to calculate for complex acid‐base solutions. Visual Water is a powerful software tool that provides accurate pH calculations with automated mathematical uncertainty analysis. Its workflow is presented and validated using acids and bases, showing a deviation of < 0.2 pH units between measured and calculated pH values. This highlights the software's reliability, which can help to simplify the work of non‐experts in water chemistry. [ABSTRACT FROM AUTHOR]

Acquiring comprehensive insights into soil properties at various spatial scales is paramount for effective land management, especially within small catchment areas that often serve as vital pastured landscapes. These regions, characterized by the intricate interplay of agroforestry systems and livestock grazing, face a pressing challenge: mitigating soil degradation while optimizing land productivity. This study aimed to analyze the spatial distribution of eight topsoil (0–5 cm) properties (clay, silt, sand, pH, cation exchange capacity, available potassium, total nitrogen, and soil organic matter) in a small grazed catchment. Four machine learning algorithms—Random Forest (RF), Support Vector Machines (SVM), Cubist, and K-Nearest Neighbors (kNN)—were used. The Boruta algorithm was employed to reduce the dimensionality of environmental covariates. The model's accuracy was assessed using the Concordance Correlation Coefficient (CCC) and Root Mean Square Error (RMSE). Additionally, uncertainty in predicted maps was quantified and assessed. The results revealed variations in predictive model performance for soil properties. Specifically, kNN excelled for clay, silt, and sand content, while RF performed well for soil pH, CEC, and TN. Cubist and SVM achieved accuracy in predicting AK and SOM, respectively. Clay, silt, CEC, and TN yielded favourable predictions, closely aligning with observations. Conversely, sand content, soil pH, AK, and SOM predictions were slightly less accurate, highlighting areas for improvement. Boruta algorithm streamlined covariate selection, reducing 23 covariates to 10 for clay and 4 for soil pH and AK prediction, enhancing model efficiency. Our study revealed spatial uncertainty patterns mirroring property distributions, with higher uncertainty in areas with elevated content. Model accuracy varied by confidence levels, performing best at intermediate levels and showing increased uncertainty at extremes. These findings offer insights into model capabilities and guide future research in soil property prediction. In conclusion, these results urge more research in small watersheds for soil and territorial management. [ABSTRACT FROM AUTHOR]

Groundwater resources is not only important essential water resources but also imperative connectors within the intricate framework of the ecological environment. High nitrate concentrations in groundwater can exerting adverse impacts on human health. It is imperative to accurately delineate the distribution characteristics of groundwater nitrate concentrations. Four different machine learning models (Gradient Boosting Regression (GB), Random Forest Regression (RF), Extreme Gradient Boosting Regression (XG) and Adaptive Boosting Regression (AD)) which combine spatial environmental data and different radius contributing area was developed to predict the distribution of nitrate concentration in groundwater. The models use 595 groundwater samples and included topography, remote sensing, hydrogeological and hydrological, climate, nitrate input, and socio-economic predictor. Gradient Boosting Regression model outperforms the other models (R2 = 0.627, MAE = 0.529, RMSE = 0.705, PICP = 0.924 for test dataset) under 500 m radius contributing area. A high-resolution (1 km) groundwater nitrate concentration distribution map reveal in the majority of the study area, groundwater nitrate concentrations are below 1 mg/L and high nitrate concentration (>10 mg/L) proportion in southeast, northeast and central main urban area karst valley regions is 1.89%, 0.91%, and 0.38% respectively. In study area, hydrogeological conditions, soil parameters, nitrogen input factors, and percentage of arable land are among the most influential explanatory factors. This work, serving as the inaugural application of utilizing effective spatial methods for predicting groundwater nitrate concentrations in Chongqing city, furnish decision-making support for the prevention and control of groundwater pollution, particularly in areas primarily dependent on groundwater for water supply and holds profound significance as a milestone achievement.

A wide diversity of regulatory practices for wastewater resource recovery plants exists throughout the world. This contribution aims to highlight the implications of choosing particular permitting structures and investigate the equivalence of effluent standards in terms of limit values and compliance assessment specifications. These factors heavily affect the true performance that a treatment plant has to attain and thus the required plant capacity and operation. The dynamic simulations executed in this work, based on a realistic case study and three selected permits from China, The Netherlands and the USA, show the impact of certain compliance specifications like sampling frequency, averaging and tolerable permit exceedances leading to differences in the required design capacity of more than 250% for the same wastewater to be treated. The results also reveal clear differences between permits in their capacity to handle excess variability. The latter is important to avoid overdesign, i.e., when further investment in treatment capacity would result only in marginal effluent quality gains, as well as to create a safe space for testing innovative technologies or ways of operation that might otherwise trigger compliance issues. HIGHLIGHTS A dynamic modelling procedure to test the equivalence of treatment plant effluent standards has been developed.; The proposed approach allows quantifying the interactions between regulations, plant design and effluent compliance.; Analysis of three actual permit structures covering a variety of global regulatory practices indicates the importance of compliance specifications like sampling frequency, averaging or tolerable permit exceedances.; Strict, not-to-exceed limit values and short averaging periods for effluent parameters tend to lead to costly, overdesigned plants without guaranteeing full compliance under all circumstances.; Permit structures that are able to handle uncertainty and variability can help foster the introduction of new innovative technologies or operational procedures.;

Abstract Background The Qinghai-Tibet Plateau is the “sensitive area” of climate change, and also the “driver” and “amplifier” of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale. Results Our results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30–70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source). Conclusions The estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community’s understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve “carbon neutrality” by 2060.

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

Monitoring irrigation is crucial for sustainable water management in freshwater-limited regions. Even though soil moisture (SM)-based inversion algorithms have been widely used to estimate irrigation, scarcity of irrigation records has prevented a thorough understanding of their uncertainties, especially in humid regions. This study assesses the suitability of the SM2RAIN algorithm for estimating irrigation at field scale using high-temporal-resolution data from four corn growing experiments conducted in north-central Florida. Daily irrigation estimates were compared with observations, revealing root mean squared differences of 1.26 to 3.84 mm/day and Nash–Sutcliffe Efficiencies of 0.33 to 0.89. The estimates were more sensitive to uncertainties in static inputs of porosity, saturation moisture and soil thickness than they were to noise in time series inputs. Defining the saturation moisture as porosity made the algorithm insensitive to both parameters, while increasing soil thickness from 40 to 200 mm improved detection accuracies by 34–46%. In addition, the impact of SM on the estimations was investigated based on satellite overpass times. The analysis showed that morning passes produced more accurate estimates for the study site, while evening passes doubled the uncertainty. This study enhances the understanding of the SM2RAIN algorithm for irrigation estimation in subtropical humid conditions, guiding future high-resolution applications.

Water contamination resulting from coal spills is one of the largest environmental problems affecting communities in the Appalachia Region of the United States. This coal slurry contains potentially toxic substances, such as hydrocarbons, heavy metals, and coal cleaning chemicals, and its leakage into water bodies (lakes, rivers, and aquifers) can lead to adverse health effects not only for freshwater bodies and plant life but also for humans. This study focused on two major experiments. The first experiment involved the use of biochar to create a biochar–polysulfone (BC-PSf) flat-sheet multifunctional membrane to remove organic contaminants, and the other major experiment compared eco-friendly (gamma-valerolactone—GVL; Rhodiasolv® PolarClean—PC) and petroleum-derived solvents (i.e., N-methyl-pyrrolidone—NMP) in the fabrication of the biochar–polysulfone membranes. The resulting membranes were tested for their efficiency in removing both positively and negatively charged organic contaminants from the collected water at varying pH values. A comparative life cycle assessment (LCA) with accompanying uncertainty and sensitivity analyses was carried out to understand the global environmental impacts of incorporating biochar, NMP, GVL, and PC in the synthesis of PSf/NMP, BC-PSf/NMP, PSf/GVL, BC-PSf/GVL, PSf/PC, and BC-PSf/PC membranes at a set surface area of 1000 m2. The results showed that the addition of biochar to the membrane matrix increased the surface area of the membranes and improved both their adsorptive and mechanical properties. The membranes with biochar incorporated in their matrix showed a higher potential for contaminant removal than those without biochar. The environmental impacts normalized to the BC-PSf/GVL membrane showed that the addition of biochar increased global warming impacts, eutrophication, and respiratory impacts by over 100% in all the membrane configurations with biochar. The environmental impacts were highly sensitive to biochar addition (Spearman’s coefficient > 0.8). The BC/PSf membrane with Rhodiasolv® PolarClean had the lowest associated global environmental impacts among all the membranes with biochar. Ultimately, this study highlighted potential tradeoffs between functional performance and global environmental impacts regarding choices for membrane fabrication.

This work proposes a new methodology to identify and validate deep learning models for artificial oil lift systems that use submersible electric pumps. The proposed methodology allows for obtaining the models and evaluating the prediction's uncertainty jointly and systematically. The methodology employs a nonlinear model to generate training and validation data and the Markov Chain Monte Carlo algorithm to assess the neural network's epistemic uncertainty. The nonlinear model was used to overcome the limitations of the need for big datasets for training deep learning models. However, the developed models are validated against experimental data after training and validation with synthetic data. The validation is also performed through the models' uncertainty assessment and experimental data. From the implementation point of view, the method was coded in Python with Tensorflow and Keras libraries used to build the neural Networks and find the hyperparameters. The results show that the proposed methodology obtained models representing both the nonlinear model's dynamic behavior and the experimental data. It provides a most probable value close to the experimental data, and the uncertainty of the generated deep learning models has the same order of magnitude as that of the nonlinear model. This uncertainty assessment shows that the built models were adequately validated. The proposed deep learning models can be applied in several applications requiring a reliable and computationally lighter model. Hence, the obtained AI dynamic models can be employed for digital twin construction, control, and optimization.

Background: The Qinghai-Tibet Plateau is the "sensitive area" of climate change, and also the "driver" and "amplifier" of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale. Results: Our results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30–70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source). Conclusions: The estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community's understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve "carbon neutrality" by 2060. Highlights: This study designed a carbon sink estimation method for Qinghai-Tibet Plateau by integrating machine learning and multiple scale ground- and remote sensed-based data. The estimated total carbon sink of the Qinghai-Tibet Plateau is 22.3 Tg C/year, accounting for about 10% of China's total carbon sink. The carbon sink of former estimation maybe greatly underestimated due to the underestimation of carbon input in the Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

Thermal processing equipment used in the aerospace industry must meet the requirements of the processes for which they are intended. The periodic tests performed by calibration laboratories, according to the AMS2750 specification, are intended to ensure compliance with these equipment requirements. While this specification does not explicitly state the need for uncertainty calculation, it does specify that pyrometry laboratories must have an ISO/IEC 17025 Quality System accredited by a recognized regional body which is a member of the International Laboratory Accreditation Cooperation (ILAC). Therefore, the calculation of uncertainties is necessary. This work presents a methodology for conducting temperature uniformity surveys and uncertainty assessment in these tests. This methodology has been applied to four different types of equipment to analyse, in each case, the contributions of uncertainties and to assess their potential for improvement. The objectives that laboratories should aim for include improving measurement accuracy and reducing uncertainty components in order to meet the criteria of both AMS2750 and ISO/IEC 17025. [ABSTRACT FROM AUTHOR]

Due to much lower initial and operating costs, as well as a great environmental and energy performance, there has been a growing tendency towards the application of solar still desalination systems to deal with water scarcity issues. By taking advantage of higher investments and providing incentives to policy makers, the application could be even broader. In order to convince the policy makers and investors, it is important to provide a clear and realistic overview of the technical, economic, and environmental viability of solar stills, and several studies have evaluated them from different viewpoints. Nonetheless, the economic and environmental factors have uncertainties, which have not been taken into account. Therefore, this study uses the Monte Carlo approach to consider the effects of the uncertainty of inflation and discount rates, in addition to emission factors, on the system's techno-enviro-economic viability. The study is performed by covering cost per liter (CPL) and the annual saving of CO2 (SCO2) as the most important key techno-economic and environmental indicators of the system. The results show that the best probability distribution functions for inflation, discount, and emission factors are normal, log-normal, and their summation, respectively. Furthermore, both SCO2 and CPL are found to have considerable uncertainty. The former has a variation ranging from 317.7 to 427.9 g, while the corresponding values for the latter are 0.0212 to 0.0270 $ · L−1, respectively. With the amounts of 0.1716 and 0.1727, the values of 378.9 g and 0.0245 $ · L−1 are the values with the highest chance of occurrence for SCO2, as well as for CPL, respectively. [ABSTRACT FROM AUTHOR]

The paper deals with the modern methods of testing and diagnostics of existing, mainly steel, bridges. The focus is on the DIC method (digital image correlation) and its application for the static measurements and dynamic measurements of the bridge deflection at high speeds and on the detection of the behavior of bridge details, such as pins and fatigue cracks. The sources of potential related error values are identified and possible strategies for the minimization of errors corresponding to the DIC field measurements are developed and described in the paper. The proposed DIC methods were successfully applied on the deflection measurement on long distances and on the measurement of the strain on details in‐situ. Relevant applications were done in the Czech Republic as a part of the bridge load testing and the results are discussed in the paper. [ABSTRACT FROM AUTHOR]

The strategic planning of open pit operations defines the best strategy for extraction of the mineral deposit to maximize the net present value. The process of strategic planning must deal with several sources of uncertainty; therefore, many authors have proposed models to incorporate it at each of its stages: Computation of the ultimate pit, optimization of pushbacks, and production scheduling. However, most works address it at each level independently, with few aiming at the whole process. In this work, we propose a methodology based on new mathematical optimization models and the application of conditional simulation of the deposit for addressing the geological uncertainty at all stages. We test the method in a real case study and evaluate whether incorporating uncertainty increases the quality of the solutions. Moreover, we benefit from our integrated framework to evaluate the relative impact of uncertainty at each stage. This could be used by decision-makers as a guide for detecting risks and focusing efforts. [ABSTRACT FROM AUTHOR]

Measurement of flow is crucial for assuring product quality, increasing manufacturing effectiveness, and promoting the development of science and technology. With the advancement of calibration and automation, standard devices using the mass method, volumetric method, and master meter method have limitations, such as low calibration efficiency and automation, large size, and complex operation. Innovations in this area are desperately needed. To realize the automation of calibrating ultrasonic water meters, a piston-type flow standard device calibration system with a high degree of automation, high calibration efficiency, small size, and easy operation was designed. A piston-type flow standard device was designed, the standard device was modeled, the selection of the main hardware and the design of the automated control of the hardware parts were completed; an automation control system adapted to the flow standard device was developed; and, furthermore, a water meter flow point calibration algorithm integrating the start–stop method and the dual-time method, as well as a water meter flow correction algorithm, was devised to improve the efficiency of ultrasonic water meter calibration. An uncertainty assessment of the designed system was completed; the standard uncertainty and expanded uncertainty of the device were 0.013% and 0.026%. Meanwhile, flow calibration tests were conducted, validating the rationality of the automated calibration algorithm for ultrasonic water meters. The results show that ultrasonic water meters calibrated with flow correction have a flow error within ±3% in the "low flow range" and within ±2% in the "high flow range", with a repeatability of less than 0.05%. This indicates that a piston-type flow standard device, coupled with an automation calibration control system, can efficiently, accurately, and conveniently perform water meter calibration, and the system has good practical value. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) of waste treatment processes is often associated with considerable uncertainties. The aim of this study is to estimate the total uncertainty in the modelled composting system and the influence of material and process parameters on the uncertainty. Four composting combinations with fresh (FC) and mature substrate compost (MSC) from partially enclosed (PEC) and open composting (OC) were investigated. Perturbation analysis was used to determine the effect of parameters on the result and Monte Carlo simulation was used to estimate the total uncertainty. This study showed that the production of MSC using PEC had the lowest overall impacts across all impact categories except ozone depletion. Results of the Monte Carlo simulation showed that comparing composting options was challenging. The sensitivity ratios obtained from the perturbation analysis showed that the process parameter percentage of carbon fraction degraded was the most influential for FC. In MSC, the moisture content in the input material and the substitution factor used for peat were the most influential. Monte Carlo simulations demonstrated the overall uncertainty of the model and its relevance when comparing results between combinations. The perturbation analysis identified the parameters that required more accurate data to reduce the uncertainty in the model. [ABSTRACT FROM AUTHOR]

The model updating procedures employed in vibration-based health monitoring need to be reliable and computationally efficient. The computational time is a fundamental task if the results are used to evaluate, in quasi-real-time, the safe or the unsafe state of strategic and relevant structures. The paper presents an efficient two-step procedure for the identification of the mechanical parameters and for the assessment of the corresponding uncertainty in model updating problems. The first step solves a least squares problem, providing a first estimate of the unknown parameters. The second (iterative) step produces a refinement of the solution. Moreover, by exploiting the error propagation theory, this article presents a direct (non-iterative) procedure to assess the uncertainty affecting the unknown parameters starting from the experimental data covariance matrix. To test the reliability of the procedure as well as to prove its applicability to structural problems, the methodology has been applied to two test-bed case studies. Finally, the procedure has been used for the damage assessment in an existing building. The results provided in this article indicate that the procedure can accurately identify the unknown parameters and properly localize and quantify the damage.

Increasing frequency of extreme rainfall induced catastrophic urban flood events in recent decades demands proactive efforts to assess flood risk and vulnerability. Here, we develop an integrative framework for fine spatiotemporal scale assessment of urban flood response to historical and future projected changes in extreme precipitation. The framework includes three main components—nonstationary modeling of historical extreme precipitation, modeling of future precipitation, and urban flood simulations. It also provides robust estimates of uncertainty in design precipitation from statistical modeling, multiple climate models and stochastic uncertainty, estimated using machine learning techniques. We demonstrate the proposed framework for White Oak Bayou watershed in Houston, Texas, US. Two‐dimensional hydrologic‐hydraulic Interconnected Channel and Pond Routing model is used to simulate flood response from design precipitation for historical (1986–2020) and two future (2021–2050 and 2071–2100) periods in three (SSP1‐2.5, SSP2‐4.5, and SSP5‐8.5) future climate scenarios. Results show that nonstationary design estimates for historical precipitation are 14%–25% higher than the stationary estimates for 100‐year event of 1‐ to 24‐hr duration. Contrary to general global trends, we found a significant reduction in future design precipitation in all three emission scenarios. Additionally, stochastic uncertainty in future design precipitations is found to be larger than the modeling uncertainty in historical estimates and climate model uncertainty in future estimates. Flood response in terms of peak flood and total flood volume suggests that the difference in stationary versus nonstationary historical and future design precipitation are substantial to cause considerable change in simulated flood. Plain Language Summary: Global warming induced increase in extreme precipitation events has been the primary cause of frequent catastrophic floods in the recent decades—majorly impacting the urban agglomerations. Informed flood mitigation planning requires adequate consideration of these changes in extreme precipitation. So far, researchers have developed methodologies to model flood response to either the historical observed or future projected change in precipitation, but not for simultaneous consideration of both. Regional‐scale evidence shows that the changes in historical observations can be inconsistent or sometimes contrasting to the changes in future projections from global climate models. Therefore, a collective assessment that includes the information of both historical and future changes in extreme precipitation is crucial. Here, we develop a comprehensive framework that incorporates both historical as well as future projected changes in extreme precipitation for fine spatiotemporal scale assessment of urban flood response. The framework also provides robust estimates of uncertainty from multiple components involved in the methodology which allows a reliable communication of attained information to stake holders and water resources planners for efficient decision making. The framework is independent of any regional assumption, thus can be used with confidence for other major cities of the world. Key Points: A framework is proposed for fine‐scale assessment of urban floods that incorporates historical as well as future changes in precipitationIt also provides estimates of uncertainty from statistical modeling of extremes, multiple climate models, and stochastic uncertaintyThe framework is demonstrated for an urbanized watershed in Houston, Texas, United States to answer two important research questions [ABSTRACT FROM AUTHOR]

The wealth of Sentinel‐1 data in eastern Tibet allows to discuss whether InSAR‐derived velocity maps measure ground motion in the ITRF reference frame with sufficient accuracy for large‐scale tectonic applications. High elevation, moderate relief and low vegetation cover in eastern Tibet ensure reliable multi‐temporal InSAR results. We automatically process seven 1,200 km long ascending and descending orbits, divided into 2 or 3 overlapping segments along‐track. Linear phase ramps in range and azimuth are removed from interferograms. The ramps and the flattened interferograms are separately inverted into time series and analyzed. The ramps are modeled by their Solid Earth Tides (SET) contribution, seasonal variations, and a trend in time. SET oscillations are clearly visible in the ramp time series. Seasonal tidal amplifications of probable atmospheric origin are also evidenced. Ramp rates are shown to precisely quantify the plate motion of each segment in the ITRF reference frame. They are thus added back to the velocity maps before along‐track merging. Then, a simple referencing procedure implying only a constant and a tilt in azimuth adjusted for each track is constrained by horizontal GNSS data. It allows straightforward vertical and horizontal decomposition. The quality of the merged and referenced data sets can then be quantitatively assessed at 1 mm/yr by the differences between InSAR and GNSS velocities and by the InSAR residuals on track overlaps. The vertical velocity field is dominated at first order by the effect of permafrost degradation, while the horizontal velocity highlights strain accumulation across major strike‐slip faults in eastern Tibet. Plain Language Summary: The imagery acquired by the Sentinel‐1 satellites allows to map the earth surface displacement field with an unprecedented resolution in space and time. The radar interferometric technique (InSAR) allows to form time‐series of electromagnetic wave delays between the satellite and the ground for each pixel of the radar footprint. We here analyze in details the differential delay time series at very large scale, that is, between the east and western parts (250 km apart), or between the southern and northern parts (700 km apart) of 30 satellite footprints in Eastern Tibet. We show that this differential delays are explained by the displacements due to tides of the Solid Earth and due to plate tectonic drift with respect to the orbit reference frame. The robust analysis of the largest‐scale delay measurements allows to tie the InSAR velocity measurements to a reference frame with a minimum input from GNSS observations. InSAR‐GNSS velocity measurements can then be compared and show an agreement within 1.4 mm/yr. Velocity maps from ascending and descending tracks are combined to obtain horizontal and vertical velocities. The maps display clearly the interseismic motion on faults and the effect of permafrost degradation and interannual hydrological fluctuations. Key Points: Phase ramps in Sentinel‐1 interferograms are dominated by solid earth tides, seasonal signals and a linear term due to plate motionThe simple tying of InSAR velocity maps in ITRF makes them mostly independent from GNSS data at large‐scale, with an agreement of 1.4 mm/yrHorizontal and vertical velocity maps show interseismic motion and the effect of permafrost degradation and hydrologic fluctuations [ABSTRACT FROM AUTHOR]

The physiography and climate of the Higher Himalayas are uniquely characterised. Some phenomenal changes, such as variability of glacier cover, lake area and glacier retreat are dynamic and are inevitable due to global temperature rises. This study is conducted using remote-sensing data. A total of 17 glaciers are mapped using Sentinel-2 (2020), Landsat 5 (1994) and Corona declassified (1966) data. In 1966, the total area of glaciers was 74.309±0.1478 km², which decreased to 72.072±0.370 km² in 2020. The estimated total loss of glacier area in 54 years (1966-2020) is 2.236±0.016 km². The average total retreat of terminii in 54 years is 439.30±13.795 m and the annual retreat is 7.91±0.255 m. In the same valley, six major glacial lakes were also observed to extend their surface, with the most expanding lake (GLK - 5) expanding by 2,010.7±30.26% in 54 years. [ABSTRACT FROM AUTHOR]

Multitemporal InSAR is a widely used geodetic technique for measuring ground deformation. However, assessing the accuracy of InSAR deformation results is challenging, especially when field measurements such as leveling are limited in coverage or unavailable. While many studies have attempted to calculate the uncertainty of deformation using a priori InSAR stochastic models to assess the deformation uncertainty, these models are often biased by various factors. In this letter, we propose a new method called the sparse parameter model (SPM) for InSAR deformation retrieval and uncertainty assessment when instantaneous deformation is not the focus. The method estimates the sparser deformation time series and leverages redundant SAR observations for the deformation uncertainty assessment and decorrelation noise suppression. The proposed model is tested by both simulated and real Sentinel-1 datasets and the derived deformation was validated with GPS measurements in the real application. The results demonstrated that the overall uncertainty of InSAR deformation, as estimated by the SPM, is 5.4 mm, falling well within the expected range of uncertainty, which highlights the effectiveness of the SPM in retrieving InSAR deformation and assessing uncertainty. [ABSTRACT FROM AUTHOR]

The geological model plays an important role in geophysics and engineering geology. The data source of geological modeling comes from interpretation data, borehole data, and outcrop data. Due to economic and technical limitations, it is impossible to obtain highly accurate and high-density data sources. The sparsity and inaccuracy of data sources lead to the uncertainty in geological models. Unlike the problem of probability, there is not enough samples for a geological model. Spatial diffusion model and merging model are introduced, which are more satisfied with the cognition of uncertainty than the existing methods. And then, using conditional information entropy, a quantification method of geological uncertainty, is proposed. Compared with the approaches of information entropy, this method took full account of the constraints of geological laws. Based on the uncertainty models and conditional information entropy, a framework of uncertainty assessment in geological models is established. It is not necessary in our framework to create multiple geological models, which is a time-consuming and laborious task. The application of Hashan survey located at north of China shows that the method and framework of this study are reasonable and effective.

Advanced wells play a crucial role in maximizing the efficiency of oil production. To achieve a successful design for advanced wells, several parameters must be considered and evaluated. Uncertainty in these parameters can have a significant impact on the performance assessment of the well in its lifetime. Absolute permeability, relative permeability, and permeability anisotropy are the parameters that determine reservoir permeability and are among the most important design parameters to be considered. This paper aims to assess the performance of advanced wells completed with passive and reactive downhole Flow Control Devices (FCDs) under uncertainty in the reservoir permeability parameters. The assessment is conducted through a case study on a synthetic heavy oil reservoir with a strong water drive. The EclipseSM reservoir simulator is used as a simulation tool and the Latin Hypercube Sampling (LHS) approach is applied as a sampling tool for uncertainty analysis in this study. According to the obtained results, under the presence of uncertainty, advanced wells with Autonomous Inflow Control Device (AICD) and Autonomous Inflow Control Valve (AICV) completions are able to mitigate the risk of water production by 53.20% and 71.12% respectively compared to conventional wells. However, Inflow Control Device (ICD) completion can only reduce the risk by 0.87%.

Data-driven manufacturing in Industry 4.0 demands digital metrology not only to drive the in-process quality assurance of manufactured products but also to supply reliable data to constantly adjust the manufacturing process parameters for zero-defect manufacturing processes. Better quality, improved productivity, and increased flexibility of manufacturing processes are obtained by combining intelligent production systems and advanced information technologies where in-process metrology plays a significant role. While traditional coordinate measurement machines offer strengths in performance, accuracy, and precision, they are not the most appropriate in-process measurement solutions when fast, non-contact and fully automated metrology is needed. In this way, non-contact optical 3D metrology tackles these limitations and offers some additional key advantages to deploying fully integrated 3D metrology capability to collect reliable data for their use in intelligent decision-making. However, the full adoption of 3D optical metrology in the manufacturing process depends on the establishment of metrological traceability. Thus, this article presents a practical approach to the task-specific uncertainty assessment realisation of a dense point cloud data type of measurement. Finally, it introduces an experimental exercise in which data-driven 3D point cloud automatic data acquisition and evaluation are performed through a model-based definition measurement strategy.

Climate change will likely increase the frequency and magnitude of extreme precipitation events and floods, increasing design peak flows that could lead to underestimates in current spillway capacity. Therefore, new methodologies for hydrological dam safety assessment considering climate change are required. This study presents a methodology that considers the impact of climate change on both inflow hydrographs and initial reservoir water levels. Moreover, the uncertainty in the procedure is assessed. The methodology is applied to the Eugui Dam in the River Arga catchment (Spain). An ensemble of 12 climate models is used. The results show an increase in the maximum reservoir water level during flood events and in the overtopping probability in the Representative Concentration Pathway 8.5 (RCP 8.5 scenario), especially in the 2071–2100 time window. The proposed methodology can be useful to assess future hydrological dam safety, fulfilling the requirements of recent regulations to consider the impact of climate change on dams. [ABSTRACT FROM AUTHOR]

An accurate model of proton exchange membrane fuel cell (PEMFC) is essential for its characterization, performance analysis, and design of optimal control strategies. However, due to various disturbances and measurement noise in practical operation, a PEMFC presents high stochasticity and parameter uncertainty. Therefore, a semi-empirical output voltage model parameter estimation method based on variational Bayes (VB) PEMFC is proposed and combined with the Sobol sensitivity analysis method to analyze the relationship between the parameters of the model to be identified and the effect of the output voltage of the model under different noise and operating conditions. The numerical results show that the VB method is able to quantify the uncertainty of the parameter estimation results and has higher computational accuracy compared with the expectation maximization (EM) method. Compared with the Markov chain Monte Carlo (MCMC) method, the VB method is able to greatly reduce the computational effort and takes less time while satisfying the accuracy. Meanwhile, the sensitivity of the model parameters to be identified to the output voltage of the model under different noise and operating conditions is quantified using the Sobol method, which explains the variation of the posterior probability distribution results obtained using the VB method under different noise and operating conditions. • Variational Bayes method is proposed to estimate parameters of PEMFC model. • Posterior distribution is obtained by automatic differential variational inference. • The uncertainty caused by noise and interference is quantified. • Higher calculation accuracy compared with EM method. • Higher computational efficiency compared with MCMC method. [ABSTRACT FROM AUTHOR]

Real-time error correction is an effective measure to improve forecast accuracy. This paper develops a real-time error correction model based on machine learning (ML) ensemble method, including the establishment of base learners, heterogeneity test, and Stacking combination. Then the Copula-based Bayesian processor of forecast (BPF) is adopted for probabilistic forecasts, which quantitatively describes the uncertainty of the correction results. Finally, the model performance is comprehensively evaluated from accuracy and stability, with deterministic metrics used to evaluate the correction effect and uncertain metrics for assessing probabilistic forecasts. The proposed model is applied to the multireservoir system in the Huai River Basin, and the results reveal the following: (1) Unlike the single ML algorithm with the performance of oscillations, the Stacking ensemble method can aggregate the advantages of multiple learners, showing robust correction effect and high adaptability across all data samples. (2) The forecasting error is significantly reduced by the Stacking method, with the average Nash–Sutcliffe efficiency coefficient ( NSE ) value increasing above 0.9, which is 4.93% higher than that of the autoregressive (AR) method. The greater superiority is also shown in the remaining evaluation metric values. Moreover, as the lead time increases, the performance of the stacking method tends to have a slower decline trend than the AR method. (3) The changes in the structure of the Stacking method have a relatively small influence on the forecast uncertainty, with all the Containing ratio ( CR ) values over 80% for different samples. The flexible combination of ML algorithms in the ensemble method will not add additional uncertainty factors and ensure the stability of the correction performance. The framework for real-time error correction and its uncertainty assessment has overall optimal correction performance with less observed data and lower computational cost required, which is promising for further improving the accuracy and reliability of real-time flood forecasting. [ABSTRACT FROM AUTHOR]

Floods represent one of the major challenges facing human societies. The impacts caused by them are extensive, and efficient flood adaptation will play a key role in the current century. The present work proposes a new approach to decision‐making in flood adaptation aimed at achieving robust adaptation decisions. It consists in evaluating decisions on a triaxial basis that considers expectation, probability quantile and investment in a so‐called expectation–quantile–investment (EQI) evaluation. With such approach it is possible to understand the influence of uncertainties in the probabilistic response of adaptation. While adaptation should minimise expected total costs, it needs also to be robust for competitive levels of investment. Transfer curves that relate events with outcomes are used to infer on the probabilistic character of decisions in a case study of a vulnerable location. Results show that EQI‐informed decisions can distinguish optimality in apparently competitive measures. It is shown that decisions based on one‐dimensional or two‐dimensional aspects of this triaxial rationale may fail to identify important probabilistic outcomes of adaptation, such as, the possibility of large losses with relevant probability of occurrence; and that are of high relevance when uncertainty considerations need to be enclosed in the analysis of adaptation. [ABSTRACT FROM AUTHOR]

Purpose: Healthy soil and the environment rely on practical risk assessment, controls to improve environmental performance, and the efficient application of receptor models. The primary focus of the study is to evaluate multiple receptor models used to estimate source distribution. Methods: This study collected 115 soil samples from the Frydek Mistek district of Czech Republic. Potentially toxic element (PTE) (Pb, As, Cr, Cd, Ni, Mn, Cu, and Zn) concentrations were measured using inductively coupled plasma optical emission spectrometry. Pollution indices like pollution index, ecological risk, geoaccumulation index, and enrichment factor were hybridized with positive matrix factorization (PMF) to create pollution index-PMF (PI-PMF), ecological risk-PMF (ER-PMF), geoaccumulation index-PMF (IGEO-PMF), and enrichment factor-PMF (EF-PMF). ABC composite mapping technique was used to visualize and determine the interaction between PTEs. Results: The use of composite mapping multimaps with varying color patterns aided in identifying and establishing the source relationship between PTEs. Pollution assessment-based receptor models (PAB-RMs) revealed that the EF-PMF outperformed the PI-PMF, ER-PMF, IGEO-PMF, and PMF receptor models. EF-PMF outperformed other receptor models in model assessments such as coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The hybridized receptor models performed better in terms of error reduction, as the PAB-RMs' reduction of DISP (displacement) intervals ratio showed smaller intervals than the parent model. Conclusion: The combination of PMF and pollution assessment indices yielded positive results. By optimizing efficiency and reducing error, the current study provides a more reliable receptor model for estimating source distribution. [ABSTRACT FROM AUTHOR]

An increasing share of dispatchable renewable generation is required to achieve energy decarbonisation goals and ensure a reliable supply to power grids. Concentrating solar power (CSP) plants hybridised with biomass boilers are promising alternatives to replace part of the peaking and baseload power generated from fossil fuel-based systems. This paper includes data related to the design variables, equations, valuation parameters and detailed results that support the research article “Market profitability of CSP-Biomass hybrid power plants: Towards a firm supply of renewable energy.” The profitability assessment is based on integrating the hourly variation of electricity prices in the Iberian day-ahead market (MIBEL) to the results of the techno-economic model through a novel economic metric named Profitability Factor. In addition, stochastic simulations were conducted to capture the uncertainty of relevant input variables on the profitability of the proposed hybrid plants. The resulting datasets presented in this paper will provide insights for researchers looking to address the economic performance of renewable generation concepts from a market profitability approach. Furthermore, the data can be used by investors and policymakers to better understand the risks and implications associated with the profitability potential of these systems.

Environmental perception plays a fundamental role in decision-making and is crucial for ensuring the safety of autonomous driving. A pressing challenge is the online evaluation of perception uncertainty, a crucial step towards ensuring the safety and the industrialization of autonomous driving. High-definition maps offer precise information about static elements on the road, along with their topological relationships. As a result, the map can provide valuable prior information for assessing the uncertainty associated with static elements. In this paper, a method for evaluating perception uncertainty online, encompassing both static and dynamic elements, is introduced based on the high-definition map. The proposed method is as follows: Firstly, the uncertainty of static elements in perception, including the uncertainty of their existence and spatial information, was assessed based on the spatial and topological features of the static environmental elements; secondly, an online assessment model for the uncertainty of dynamic elements in perception was constructed. The online evaluation of the static element uncertainty was utilized to infer the dynamic element uncertainty, and then a model for recognizing the driving scenario and weather conditions was constructed to identify the triggering factors of uncertainty in real-time perception during autonomous driving operations, which can further optimize the online assessment model for perception uncertainty. The verification results on the nuScenes dataset show that our uncertainty assessment method based on a high-definition map effectively evaluates the real-time perception results’ performance.

Geologic CO2 sequestration (GCS) has received high-level attention from the global scientific community as a response to climate change due to higher concentrations of CO2 in the atmosphere. However, GCS in saline aquifers poses certain risks including CO2/brine leakage through wells or non-sealing faults into groundwater or to the earth's surface. Understanding crucial reservoir parameters and other geologic features affecting the likelihood of these leakage occurrences will aid the decision-making process regarding GCS operations. In this study, we develop a science-based methodology for quantifying risk profiles at geologic CO2 sequestration sites as part of US DOE's National Risk Assessment Partnership (NRAP). We apply NRAP tools to a field scale project in a fractured saline aquifer located at Kevin Dome, Montana, which is part of DOE's Big Sky Carbon Sequestration Partnership project. Risks associated with GCS injection and monitoring are difficult to quantify due to a dearth of data and uncertainties. One solution is running a large number of numerical simulations of the primary CO2 injection reservoir, shallow reservoirs/aquifers, faults, and wells to address leakage risks and uncertainties. However, a full-physics simulation is not computationally feasible because the model is too large and requires fine spatial and temporal discretization to accurately reproduce complex multiphase flow processes. We employ the NRAP Integrated Assessment Model (NRAP-IAM), a hybrid system model developed by the US-DOE for use in performance and quantitative risk assessment of CO2 sequestration. The IAM model requires reduced order models (ROMs) developed from numerical reservoir simulations of a primary CO2 injection reservoir. The ROMs are linked with discrete components of the NRAP-IAM including shallow reservoirs/aquifers and the atmosphere through potential leakage pathways. A powerful stochastic framework allows NRAP-IAM to be used to explore complex interactions among a large number of uncertain variables and to help evaluate the likely performance of potential sequestration sites. Using the NRAP-IAM, we find that the potential amount of CO2 leakage is most sensitive to values of permeability, end-point CO2 relative permeability, hysteresis of CO2 relative permeability, capillary pressure, and permeability of confining rocks. In addition to demonstrating the application of the NRAP risk assessment tools, this work shows that GCS in the Kevin Dome has a higher probability of encountering injectivity limitations during injection of CO2 into the Middle Duperow formation than previous studies have calculated. Finally, we estimate very low risk of CO2 leakage to the atmosphere unless the quality of the legacy well completions is extremely poor.

In this study, an intelligent monitoring platform is established for continuous quantification of soil, vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and wind speed) to provide an efficient dataset for modeling suction response through machine learning. Two characteristic parameters representing suction response during wetting processes, i.e. response time and mean reduction rate of suction, are formulated through multi-gene genetic programming (MGGP) using eight selected influential parameters including depth, initial soil suction, vegetation- and atmosphere-related parameters. An error standard–based performance evaluation indicated that MGGP has appreciable potential for model development when working with even fewer than 100 data. Global sensitivity analysis revealed the importance of tree canopy and mean wind speed to estimation of response time and indicated that initial soil suction and rainfall amount have an important effect on the estimated suction reduction rate during a wetting process. Uncertainty assessment indicated that the two MGGP models describing suction response after rainfall are reliable and robust under uncertain conditions. In-depth analysis of spatial variations in suction response validated the robustness of two obtained MGGP models in prediction of suction variation characteristics under natural conditions.